Target tracking such as e.g. visual (video-based) target tracking is a topic of major relevance to a large series of computer vision domains involving monitoring, surveillance, guidance, obstacle avoidance and scene interpretation. The application domains include diverse fields such as radar control, traffic monitoring or vision-based robotics.
Tracking an object involves model descriptions of 1) how the object parameters evolve over time and as well as 2) how the estimated state of an object can be related to a sensory measurement. These two models have to be tailored to the specific object that should be tracked and its dynamics, e.g. by indicating that the object behaves in a ballistic fashion according to Newtonian physics and e.g. a sensory measurement using a characteristic, known object color.
For domain-specific applications, a single, fixed description of the involved models is sufficient. In a situation involving complex visual scenes, however, a system is needed that allows a dynamic switching and adaptation of the involved models. An example is e.g. a situation of a bouncing ball target, which moves in a ballistic fashion while falling down but rebounds when hitting the floor, making a different motion model necessary.
The current way of dealing with such situations is by introducing mixture models [1] which are treated probabilistically, allowing the tracking system to give more weight to those models that best fit with the sensory observations.
The drawback of currently available tracking models is that all the possible single models used for the mixture have to be directly integrated into the tracking process from the start, and that they have to be evaluated simultaneously.
Nevertheless, if one considers that the complexity level of objects that can be tracked is not fixed, that objects can be arranged into trackable object configurations, and that this may occur hierarchically by arranging object configurations into even larger, trackable ensembles, the potential space of tracking models becomes combinatorially large. No previously fixed mixture of tracking models can then be devised to cover the entire range of possible tracking models.